Sparse regularization for semi-supervised classification

DOI10.1016/j.patcog.2011.02.013zbMath1218.68120OpenAlexW2080509359MaRDI QIDQ2275970

Mingyu Fan, Nannan Gu, Hong Qiao, Bo Zhang

Publication date: 10 August 2011

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.02.013

zbMATH Keywords

dimensionality reductionsemi-supervised learningregularization theoryregularized least square classification

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (5)

Multi-source adaptation joint kernel sparse representation for visual classification ⋮ Semi-supervised active learning for support vector machines: a novel approach that exploits structure information in data ⋮ Convergence rate of the semi-supervised greedy algorithm ⋮ Semi-supervised classification based on low rank representation ⋮ A multikernel-like learning algorithm based on data probability distribution

Uses Software

• L1-MAGIC

Cites Work

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• Semi-supervised learning on Riemannian manifolds
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• On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
• Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
• The minimum description length principle in coding and modeling
• On Different Facets of Regularization Theory
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Stable signal recovery from incomplete and inaccurate measurements

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